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DeepCoast: Quantifying Seagrass Distribution in Coastal Water Through Deep Capsule Networks

  • Daniel PérezEmail author
  • Kazi Islam
  • Victoria Hill
  • Richard Zimmerman
  • Blake Schaeffer
  • Jiang Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11257)

Abstract

Seagrass is a highly valuable component of coastal ecosystems ecologically and economically, yet reliable mapping of seagrass density is not available due to the high cost of data processing and spatial mapping. This paper presents a deep learning approach for quantification of leaf area index (LAI) levels of seagrass in coastal water using high resolution multispectral satellite images. Specifically, a deep capsule network (DCN) is developed for simultaneous classification and quantification of seagrass based on the multispectral images. The DCN is jointly optimized for classification and regression, and is capable of performing end-to-end seagrass quantification. We separately validated the proposed method on three images taken in Florida coastal area and achieved better results with DCN when compared against a deep convolutional neural network (CNN) model and a linear regression model. In addition, transfer learning strategies are developed to transfer knowledge in a DCN trained at one location for seagrass quantification to different locations with minimum field observations, which saves a significant amount of time and resources in the mapping of seagrass LAI. Our experimental results show that the developed capsule network achieved superb performances in few-shot transfer learning as compared to direct linear regression and traditional CNN models.

Keywords

Seagrass quantification Deep learning Convolutional neural networks Capsule networks Transfer learning 

References

  1. 1.
    Banerjee, D., et al.: A deep transfer learning approach for improved post-traumatic stress disorder diagnosis. In: 2017 IEEE International Conference on Data Mining (ICDM), pp. 11–20. IEEE (2017)Google Scholar
  2. 2.
    Breuer, L., Freede, H.: Leaf area index - LAI. https://www.staff.uni-giessen.de/~gh1461/plapada/lai/lai.html (2003). Accessed 4 Oct 2018
  3. 3.
    Chowdhury, M.M.U., Hammond, F., Konowicz, G., Xin, C., Wu, H., Li, J.: A few-shot deep learning approach for improved intrusion detection. In: 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), pp. 456–462. IEEE (2017)Google Scholar
  4. 4.
    Donahue, J., et al.: DeCAF: a deep convolutional activation feature for generic visual recognition. In: International Conference on Machine Learning, pp. 647–655 (2014)Google Scholar
  5. 5.
    Hemminga, M.A., Duarte, C.M.: Seagrass Ecology. Cambridge University Press, Cambridge (2000)Google Scholar
  6. 6.
    Hill, V., Zimmerman, R., Bissett, W., Dierssen, H., Kohler, D.: Evaluating light availability, seagrass biomass and productivity using hyperspectral airborne remote sensing in Saint Joseph’s Bay, Florida. Estuaries Coasts 37(6), 1467–1489 (2014)CrossRefGoogle Scholar
  7. 7.
    Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Sig. Process. Mag. 29(6), 82–97 (2012)CrossRefGoogle Scholar
  8. 8.
    Hinton, G., Sabour, S., Frosst, N.: Matrix capsules with EM routing (2018). https://openreview.net/pdf?id=HJWLfGWRb
  9. 9.
    Islam, K., Perez, D., Hill, V., Schaeffer, B., Zimmerman, R., Li, J.: Seagrass detection in coastal water through deep capsule networks. In: Chinese Conference on Pattern Recognition and Computer Vision. Sun-Yat Sen University (2018)Google Scholar
  10. 10.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  11. 11.
    Ning, R., Wang, C., Xin, C., Li, J., Wu, H.: DeepMag: sniffing mobile apps in magnetic field through deep convolutional neural networks. IEEE (2018)Google Scholar
  12. 12.
    Oguslu, E., et al.: Detection of seagrass scars using sparse coding and morphological filter. Remote Sens. Environ. 213, 92–103 (2018)CrossRefGoogle Scholar
  13. 13.
    Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2010)CrossRefGoogle Scholar
  14. 14.
    Perez, D., et al.: Deep learning for effective detection of excavated soil related to illegal tunnel activities. In: IEEE Ubiquitous Computing, Electronics and Mobile Communication Conference (2017)Google Scholar
  15. 15.
    Perez, D., Li, J., Shen, Y., Dayanghirang, J., Wang, S., Zheng, Z.: Deep learning for pulmonary nodule CT image retrieval-an online assistance system for novice radiologists. In: 2017 IEEE International Conference on Data Mining Workshops (ICDMW), pp. 1112–1121. IEEE (2017)Google Scholar
  16. 16.
    Phinn, S., Roelfsema, C., Dekker, A., Brando, V., Anstee, J.: Mapping seagrass species, cover and biomass in shallow waters: an assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton Bay (Australia). Remote Sens. Environ. 112(8), 3413–3425 (2008)CrossRefGoogle Scholar
  17. 17.
    Sabour, S., Frosst, N., Hinton, G.E.: Dynamic routing between capsules. In: Advances in Neural Information Processing Systems, pp. 3857–3867 (2017)Google Scholar
  18. 18.
    Short, F.T., Coles, R.G.: Global Seagrass Research Methods, vol. 33. Elsevier, Amsterdam (2001)CrossRefGoogle Scholar
  19. 19.
    Wicaksono, P., Hafizt, M.: Mapping seagrass from space: addressing the complexity of seagrass LAI mapping. Eur. J. Remote Sens. 46(1), 18–39 (2013)CrossRefGoogle Scholar
  20. 20.
    Xi, E., Bing, S., Jin, Y.: Capsule network performance on complex data. arXiv preprint arXiv:1712.03480 (2017)
  21. 21.
    Yang, D., Yang, C.: Detection of seagrass distribution changes from 1991 to 2006 in Xincun Bay, Hainan, with satellite remote sensing. Sensors 9(2), 830–844 (2009)CrossRefGoogle Scholar
  22. 22.
    Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems, pp. 3320–3328 (2014)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Modeling, Simulation and Visualization EngineeringOld Dominion UniversityNorfolkUSA
  2. 2.Department of Electrical and Computer EngineeringOld Dominion UniversityNorfolkUSA
  3. 3.Department of Earth and Atmospheric SciencesOld Dominion UniversityNorfolkUSA
  4. 4.Office of Research and DevelopmentU.S. Environmental Protection AgencyCorvallisUSA

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